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Most current models assume that the perceptual and cognitive processes of visual word recognition and reading operate upon neuronally coded domain-general low-level visual representations – typically oriented line representations. We here demonstrate, consistent with neurophysiological theories of Bayesian-like predictive neural computations, that prior visual knowledge of words may be utilized to ‘explain away’ redundant and highly expected parts of the visual percept. Subsequent processing stages, accordingly, operate upon an optimized representation of the visual input, the orthographic prediction error, highlighting only the visual information relevant for word identification. We show that this optimized representation is related to orthographic word characteristics, accounts for word recognition behavior, and is processed early in the visual processing stream, i.e., in V4 and before 200 ms after word-onset. Based on these findings, we propose that prior visual-orthographic knowledge is used to optimize the representation of visually presented words, which in turn allows for highly efficient reading processes.
Most current models assume that the perceptual and cognitive processes of visual word recognition and reading operate upon neuronally coded domain-general low-level visual representations – typically oriented line representations. We here demonstrate, consistent with neurophysiological theories of Bayesian-like predictive neural computations, that prior visual knowledge of words may be utilized to ‘explain away’ redundant and highly expected parts of the visual percept. Subsequent processing stages, accordingly, operate upon an optimized representation of the visual input, the orthographic prediction error, highlighting only the visual information relevant for word identification. We show that this optimized representation is related to orthographic word characteristics, accounts for word recognition behavior, and is processed early in the visual processing stream, i.e., in V4 and before 200 ms after word-onset. Based on these findings, we propose that prior visual-orthographic knowledge is used to optimize the representation of visually presented words, which in turn allows for highly efficient reading processes.
How is semantic information stored in the human mind and brain? Some philosophers and cognitive scientists argue for vectorial representations of concepts, where the meaning of a word is represented as its position in a high-dimensional neural state space. At the intersection of natural language processing and artificial intelligence, a class of very successful distributional word vector models has developed that can account for classic EEG findings of language, i.e., the ease vs. difficulty of integrating a word with its sentence context. However, models of semantics have to account not only for context-based word processing, but should also describe how word meaning is represented. Here, we investigate whether distributional vector representations of word meaning can model brain activity induced by words presented without context. Using EEG activity (event-related brain potentials) collected while participants in two experiments (English, German) read isolated words, we encode and decode word vectors taken from the family of prediction-based word2vec algorithms. We find that, first, the position of a word in vector space allows the prediction of the pattern of corresponding neural activity over time, in particular during a time window of 300 to 500 ms after word onset. Second, distributional models perform better than a human-created taxonomic baseline model (WordNet), and this holds for several distinct vector-based models. Third, multiple latent semantic dimensions of word meaning can be decoded from brain activity. Combined, these results suggest that empiricist, prediction-based vectorial representations of meaning are a viable candidate for the representational architecture of human semantic knowledge.
The outstanding speed of language comprehension necessitates a highly efficient implementation of cognitive-linguistic processes. The domain-general theory of Predictive Coding suggests that our brain solves this problem by continuously forming linguistic predictions about expected upcoming input. The neurophysiological implementation of these predictive linguistic processes, however, is not yet understood. Here, we use EEG (human participants, both sexes) to investigate the existence and nature of online-generated, category-level semantic representations during sentence processing. We conducted two experiments in which some nouns – embedded in a predictive spoken sentence context – were unexpectedly delayed by 1 second. Target nouns were either abstract/concrete (Experiment 1) or animate/inanimate (Experiment 2). We hypothesized that if neural prediction error signals following (temporary) omissions carry specific information about the stimulus, the semantic category of the upcoming target word is encoded in brain activity prior to its presentation. Using time-generalized multivariate pattern analysis, we demonstrate significant decoding of word category from silent periods directly preceding the target word, in both experiments. This provides direct evidence for predictive coding during sentence processing, i.e., that information about a word can be encoded in brain activity before it is perceived. While the same semantic contrast could also be decoded from EEG activity elicited by isolated words (Experiment 1), the identified neural patterns did not generalize to pre-stimulus delay period activity in sentences. Our results not only indicate that the brain processes language predictively, but also demonstrate the nature and sentence-specificity of category-level semantic predictions preactivated during sentence comprehension.
Across languages, the speech signal is characterized by a predominant modulation of the amplitude spectrum between about 4.3-5.5Hz, reflecting the production and processing of linguistic information chunks (syllables, words) every ∼200ms. Interestingly, ∼200ms is also the typical duration of eye fixations during reading. Prompted by this observation, we demonstrate that German readers sample written text at ∼5Hz. A subsequent meta-analysis with 142 studies from 14 languages replicates this result, but also shows that sampling frequencies vary across languages between 3.9Hz and 5.2Hz, and that this variation systematically depends on the complexity of the writing systems (character-based vs. alphabetic systems, orthographic transparency). Finally, we demonstrate empirically a positive correlation between speech spectrum and eye-movement sampling in low-skilled readers. Based on this convergent evidence, we propose that during reading, our brain’s linguistic processing systems imprint a preferred processing rate, i.e., the rate of spoken language production and perception, onto the oculomotor system.
How is semantic information stored in the human mind and brain? Some philosophers and cognitive scientists argue for vectorial representations of concepts, where the meaning of a word is represented as its position in a high-dimensional neural state space. At the intersection of natural language processing and artificial intelligence, a class of very successful distributional word vector models has developed that can account for classic EEG findings of language, that is, the ease versus difficulty of integrating a word with its sentence context. However, models of semantics have to account not only for context-based word processing, but should also describe how word meaning is represented. Here, we investigate whether distributional vector representations of word meaning can model brain activity induced by words presented without context. Using EEG activity (event-related brain potentials) collected while participants in two experiments (English and German) read isolated words, we encoded and decoded word vectors taken from the family of prediction-based Word2vec algorithms. We found that, first, the position of a word in vector space allows the prediction of the pattern of corresponding neural activity over time, in particular during a time window of 300 to 500 ms after word onset. Second, distributional models perform better than a human-created taxonomic baseline model (WordNet), and this holds for several distinct vector-based models. Third, multiple latent semantic dimensions of word meaning can be decoded from brain activity. Combined, these results suggest that empiricist, prediction-based vectorial representations of meaning are a viable candidate for the representational architecture of human semantic knowledge.
Previous reports of improved oral reading performance for dyslexic children but not for regular readers when between-letter spacing was enlarged led to the proposal of a dyslexia-specific deficit in visual crowding. However, it is in this context also critical to understand how letter spacing affects visual word recognition and reading in unimpaired readers. Adopting an individual differences approach, the present study, accordingly, examined whether wider letter spacing improves reading performance also for non-impaired adults during silent reading and whether there is an association between letter spacing and crowding sensitivity. We report eye movement data of 24 German students who silently read texts presented either with normal or wider letter spacing. Foveal and parafoveal crowding sensitivity were estimated using two independent tests. Wider spacing reduced first fixation durations, gaze durations, and total fixation time for all participants, with slower readers showing stronger effects. However, wider letter spacing also reduced skipping probabilities and elicited more fixations, especially for faster readers. In terms of words read per minute, wider letter spacing did not provide a benefit, and faster readers in particular were slowed down. Neither foveal nor parafoveal crowding sensitivity correlated with the observed letter-spacing effects. In conclusion, wide letter spacing reduces single word processing time in typically developed readers during silent reading, but affects reading rates negatively since more words must be fixated. We tentatively propose that wider letter spacing reinforces serial letter processing in slower readers, but disrupts parallel processing of letter chunks in faster readers. These effects of letter spacing do not seem to be mediated by individual differences in crowding sensitivity.
Synesthesia is a phenomenon in which additional perceptual experiences are elicited by sensory stimuli or cognitive concepts. Synesthetes possess a unique type of phenomenal experiences not directly triggered by sensory stimulation. Therefore, for better understanding of consciousness it is relevant to identify the mental and physiological processes that subserve synesthetic experience. In the present work we suggest several reasons why synesthesia has merit for research on consciousness. We first review the research on the dynamic and rapidly growing field of the studies of synesthesia. We particularly draw attention to the role of semantics in synesthesia, which is important for establishing synesthetic associations in the brain. We then propose that the interplay between semantics and sensory input in synesthesia can be helpful for the study of the neural correlates of consciousness, especially when making use of ambiguous stimuli for inducing synesthesia. Finally, synesthesia-related alterations of brain networks and functional connectivity can be of merit for the study of consciousness.
Following a brief review of current efforts to identify the neuronal correlates of conscious processing (NCCP) an attempt is made to bridge the gap between the material neuronal processes and the immaterial dimensions of subjective experience. It is argued that this "hard problem" of consciousness research cannot be solved by only considering the neuronal underpinnings of cognition. The proposal is that the hard problem can be treated within a naturalistic framework if one considers not only the biological but also the socio-cultural dimensions of evolution. The argument is based on the following premises: perceptions are the result of a constructivist process that depends on priors. This applies both for perceptions of the outer world and the perception of oneself. Social interactions between agents endowed with the cognitive abilities of humans generated immaterial realities, addressed as social or cultural realities. This novel class of realities assumed the role of priors for the perception of oneself and the embedding world. A natural consequence of these extended perceptions is a dualist classification of observables into material and immaterial phenomena nurturing the concept of ontological substance dualism. It is argued that perceptions shaped by socio-cultural priors lead to the construction of a self-model that has both a material and an immaterial dimension. As priors are implicit and not amenable to conscious recollection the perceived immaterial dimension is experienced as veridical and not derivable from material processes—which is the hallmark of the hard problem. These considerations let the hard problem appear as the result of cognitive constructs that are amenable to naturalistic explanations in an evolutionary framework.
Thyroid hormone is a crucial regulator of gene expression in the developing and adult retina. Here we sought to map sites of thyroid hormone signaling at the cellular level using the transgenic FINDT3 reporter mouse model in which neurons express β-galactosidase (β-gal) under the control of a hybrid Gal4-TRα receptor when triiodothyronine (T3) and cofactors of thyroid receptor signaling are present. In the adult retina, nearly all neurons of the ganglion cell layer (GCL, ganglion cells and displaced amacrine cells) showed strong β-gal labeling. In the inner nuclear layer (INL), a minority of glycineric and GABAergic amacrine cells showed β-gal labeling, whereas the majority of amacrine cells were unlabeled. At the level of amacrine types, β-gal labeling was found in a large proportion of the glycinergic AII amacrines, but only in a small proportion of the cholinergic/GABAergic ‘starburst’ amacrines. At postnatal day 10, there also was a high density of strongly β-gal-labeled neurons in the GCL, but only few amacrine cells were labeled in the INL. There was no labeling of bipolar cells, horizontal cells and Müller glia cells at both stages. Most surprisingly, the photoreceptor somata in the outer nuclear layer also showed no β-gal label, although thyroid hormone is known to control cone opsin expression. This is the first record of thyroid hormone signaling in the inner retina of an adult mammal. We hypothesize that T3 levels in photoreceptors are below the detection threshold of the reporter system. The topographical distribution of β-gal-positive cells in the GCL follows the overall neuron distribution in that layer, with more T3-signaling cells in the ventral than the dorsal half-retina.